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source("tianfengRwrappers.R")

umapplot

umapplot(ds2)
ds2 <- RenameIdents(ds2,"FB" = "Fibroblast", "pericyte" = "Pericyte","fibromyocyte"="Fibromyocyte")
umapplot(ds2, group.by = "seurat_clusters")
Idents(ds2) <- factor(Idents(ds2),levels =
                        c("SMC1","Fibromyocyte","Pericyte","Fibroblast","SMC2"))
umapplot(ds2)
ds2$Classification1 <- Idents(ds2)
saveRDS(ds2,"ds2.rds")

ggsave("./fig3/ds2.svg",plot = umapplot(ds2),device = svg, width = 6, height = 5)

ds0 <- readRDS("ds0.rds")

周细胞的存在

multi_featureplot(c("FABP4", "RERGL", "NRIP2","HIGD1B"),ds2,labels = "")

基因表达 小提琴图

ds2 -> ds1

无监督聚类 harmony/CCA

viotheme <- theme(plot.title = element_text(size = 17,color="black",hjust = 0.5),
                 axis.title = element_text(size = 17,color ="black"), 
                 axis.text = element_text(size = 17,color = "black"),
                 panel.grid.minor.y = element_blank(),
                 panel.grid.minor.x = element_blank(),
                 axis.text.x = element_text(size= 17, angle = 0),
                 panel.grid=element_blank(),
                 legend.position = "top",
                 legend.text = element_text(size= 17),
                 legend.title= element_text(size= 17))

stat_theme <- stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test", size = 6,
                     label.y = max(merge_expr$expr),
                      hide.ns = F)

监督聚类 xgboost

ds2FbM <- subset(ds2,Classification1 == "Fibromyocyte")
ds1FbM <- subset(ds1,ref_celltype == "Fibromyocyte")

ds2data <- get_data_table(ds2FbM,type = "data")
ds1data <- get_data_table(ds1FbM,type = "data")


func1 <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}
merge_expr <- data.frame()
for (i in lapply(genes_to_show, func1,"ds1",ds1data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func1,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}
rownames(merge_expr) <- NULL
Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
# head(Data_summary)

ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("gene expression"),x=NULL, title = "Split violin") + 
  theme_classic()+ viotheme + stat_theme
ggobj
ggsave("./fig3/supds2tods1.png", device = png, plot = ggobj, height = 5, width = 7)

ds2 -> ds0

无监督聚类 harmony & CCA

监督聚类 xgboost

# Idents(ds0) <- ds0$ref_celltype
# umapplot(ds0,group.by = "ref_celltype")
ds2FbM <- subset(ds2, Classification1 == "Fibromyocyte")
ds0FbM <- subset(ds0, ref_celltype == "Fibromyocyte")

ds2data <- get_data_table(ds2FbM,type = "data")
ds0data <- get_data_table(ds0FbM,type = "data")

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func1,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func1,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)

ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("gene expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ viotheme + stat_theme
ggobj
ggsave("./fig3/supds2tods0.png", device = png, plot = ggobj, height = 5, width = 7)

viotheme <- theme(plot.title = element_text(size = 12,color="black",hjust = 0.5),
                     axis.title = element_text(size = 12,color ="black"), 
                     axis.text = element_text(size= 12,color = "black"),
                     panel.grid.minor.y = element_blank(),
                     panel.grid.minor.x = element_blank(),
                     axis.text.x = element_text(angle = 45, hjust = 1 ),
                     panel.grid=element_blank(),
                     legend.position = "top",
                     legend.text = element_text(size= 12),
                     legend.title= element_text(size= 12)) 

# https://stackoverflow.com/a/45614547
GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, draw_group = function(self, data, ..., draw_quantiles = NULL){
  data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
  grp <- data[1,'group']
  newdata <- plyr::arrange(transform(data, x = if(grp%%2==1) xminv else xmaxv), if(grp%%2==1) y else -y)
  newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
  newdata[c(1,nrow(newdata)-1,nrow(newdata)), 'x'] <- round(newdata[1, 'x']) 
  if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
    stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <= 
                                              1))
    quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
    aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
    aesthetics$alpha <- rep(1, nrow(quantiles))
    both <- cbind(quantiles, aesthetics)
    quantile_grob <- GeomPath$draw_panel(both, ...)
    ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
  }
  else {
    ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
  }
})

geom_split_violin <- function (mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) {
  layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}

---

scatter 比较

scatter_theme2 <- 
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

ds2 -> ds0

ref_ds2FBM <- subset(ds2,Classification1 == "Fibromyocyte")

unaligned_ds0FBM <- subset(ds0,Classification1 == "Fibromyocyte")
harmony_ds0FbM <- subset(CAD_merge_harmony, orig.ident == "ds0" & ds2_celltype == "Fibromyocyte") #harmony
CCA_ds0FbM <- subset(CAD_merge_CCA, orig.ident == "ds0" & ds2_celltype == "Fibromyocyte")

xgb_ds0FbM <- subset(ds0, ref_celltype == "Fibromyocyte")

fig.3 harmony vs xgb vs ref

ggplot(data, aes(x=LUM, y=BGN, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=TAGLN, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

supp

unaligned vs CCA vs ref vs xgb

data1 <- FetchData(object = xgb_ds0FbM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data1) <-  NULL
data1$group <- "xgb_ds0"

data2 <- FetchData(object = CCA_ds0FbM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data2) <-  NULL
data2$group <- "CCA_ds0"

data3 <- FetchData(object = unaligned_ds0FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data3) <-  NULL
data3$group <- "unaligned_ds0"

data4 <- FetchData(object = ref_ds2FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data4) <-  NULL
data4$group <- "ref_ds2"



data <- rbind(data1,data2,data3,data4)

ggplot(data, aes(x=LUM, y=BGN, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=TAGLN, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

scatter

ds2 -> ds1

ref_ds2FBM <- subset(ds2,Classification1 == "Fibromyocyte")

unaligned_ds1FBM <- subset(ds1,Classification1 == "Fibromyocyte")
harmony_ds1FbM <- subset(CAD_merge_harmony, orig.ident == "ds1" & ds2_celltype == "Fibromyocyte") #harmony
CCA_ds1FbM <- subset(CAD_merge_CCA, orig.ident == "ds1" & ds2_celltype == "Fibromyocyte")

xgb_ds1FbM <- subset(ds1, ref_celltype == "Fibromyocyte")

fig.3 harmony vs xgb vs ref

data1 <- FetchData(object = xgb_ds1FbM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data1) <-  NULL
data1$group <- "xgb_ds1"

data2 <- FetchData(object = harmony_ds1FbM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data2) <-  NULL
data2$group <- "Harmony_ds1"

data3 <- FetchData(object = ref_ds2FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data3) <-  NULL
data3$group <- "ref_ds2"

data <- rbind(data1,data2,data3)

ggplot(data, aes(x=LUM, y=BGN, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=TAGLN, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

supp

unaligned vs CCA vs ref vs xgb

data1 <- FetchData(object = xgb_ds1FbM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data1) <-  NULL
data1$group <- "xgb_ds1"

data2 <- FetchData(object = CCA_ds1FbM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data2) <-  NULL
data2$group <- "CCA_ds1"

data3 <- FetchData(object = unaligned_ds1FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data3) <-  NULL
data3$group <- "unaligned_ds1"

data4 <- FetchData(object = ref_ds2FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data4) <-  NULL
data4$group <- "ref_ds2"



data <- rbind(data1,data2,data3,data4)

ggplot(data, aes(x=LUM, y=BGN, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=TAGLN, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

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